{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python常用可视化操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/opt/conda/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/opt/conda/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "/opt/conda/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from matplotlib.font_manager import FontProperties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            x         y\n",
      "0    1.024675  2.036560\n",
      "1    0.765704 -2.793654\n",
      "2   -0.028360 -1.074671\n",
      "3    1.076851 -1.105510\n",
      "4   -0.653478  0.279665\n",
      "..        ...       ...\n",
      "495  0.124804  0.589177\n",
      "496  0.050720 -0.495406\n",
      "497 -2.488549 -1.186288\n",
      "498  0.425843  1.272024\n",
      "499 -2.814760 -0.431434\n",
      "\n",
      "[500 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 散点图\n",
    "def scatter():\n",
    "    # 数据准备\n",
    "    N = 500\n",
    "    x = np.random.randn(N)#生成0-500标准正态分布的数据\n",
    "    y = np.random.randn(N)\n",
    "    # 用Matplotlib画散点图\n",
    "    plt.scatter(x, y,marker='x')#maker代表样式\n",
    "    plt.xlabel('x')\n",
    "    plt.ylabel('y')\n",
    "    plt.title('use matplotlib')\n",
    "    #plt.show()\n",
    "    # 用Seaborn画散点图\n",
    "    df = pd.DataFrame({'x': x, 'y': y})\n",
    "    print(df)#构建了一个dataframe \n",
    "    sns.jointplot(x=\"x\", y=\"y\", data=df, kind='scatter');#之类把df喂入给data就OK了，Kind选‘scatter’\n",
    "    plt.xlabel('x')\n",
    "    plt.ylabel('y')\n",
    "    plt.title('use seaborns')\n",
    "    plt.show()\n",
    "# 散点图\n",
    "scatter()    #两个图对比发现seaborns比matplotlib更美观一些 坐标轴的比例  而且用的dataframe 这种我们常用的 数据类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 折线图\n",
    "def line_chart():\n",
    "    # 数据准备\n",
    "    x = [1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910]\n",
    "    y = [265, 323, 136, 220, 305, 350, 419, 450, 560, 720, 830]\n",
    "    # 使用Matplotlib画折线图\n",
    "    plt.plot(x, y)\n",
    "    plt.title('use matplotlib')\n",
    "    plt.show()\n",
    "    # 使用Seaborn画折线图\n",
    "    df = pd.DataFrame({'x': x, 'y': y})\n",
    "    sns.lineplot(x=\"x\", y=\"y\", data=df)#把df喂进去 \n",
    "    plt.title('use seaborns')\n",
    "    plt.show()\n",
    "line_chart()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 条形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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k+TkAb6Fss584f0mOAD5IL94PTHS6Hd9c5+/gvvXHA3dMZLI2/Nj5A3atqmVVNV1V0/TegzmhqtZObMIx8ZuY80hyKvAmYAu9T1CcD1wO7EXvTZD7qurZk5twxzbH+dsfOBzY0G3y7ao6YULj7fDmOH/fA44BHgceAs7Yyf/qipHMPn9V9dq+dZ8H/tSAS5ImxlsoktQoAy5JjTLgktQoAy5JjTLgktQoAy5JjTLgktSo/wMGScSt0wvIlAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/stu_15527388015/.local/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 条形图\n",
    "def bar_chart():\n",
    "    # 数据准备\n",
    "    x = ['c1', 'c2', 'c3', 'c4']\n",
    "    y = [15, 18, 5, 26]\n",
    "    plt.title(\"use matplotlib\")\n",
    "    # 用Matplotlib画条形图\n",
    "    plt.bar(x, y)\n",
    "    plt.show()\n",
    "    # 用Seaborn画条形图\n",
    "    sns.barplot(x, y)\n",
    "    plt.title(\"use sns\")\n",
    "    plt.show()\n",
    "bar_chart()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 箱线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.75087142 -0.09427139 -0.692092   -0.22736537]\n",
      " [-1.43442059 -0.01404541 -0.46629476 -0.16764   ]\n",
      " [ 0.68440809 -0.92897061  1.24888783 -2.46742374]\n",
      " [ 0.36983554 -1.06793707  1.47117102 -0.96202071]\n",
      " [-2.66061847  0.32267969 -1.33225073 -1.45158481]\n",
      " [ 0.67658029 -0.28332006 -0.64001738 -0.12236913]\n",
      " [-0.78645315  1.24761339 -0.35349914  1.62202707]\n",
      " [-0.37164767  0.20222686  0.77510057 -0.84843541]\n",
      " [-0.73027035  0.41828992  0.07080762  0.31449224]\n",
      " [ 0.11604952  1.1844528  -1.61383096 -0.05772601]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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blsRncZNExHHbfZJeIemJsutZD2bkLzYm6Y7673fUH2PjfhARb4qI10naI+m2jDMeoJMxI29ge0C1GeMbbIekPklh+4+Dkwkti4iDtrdLGpT0ZNn1JHRU0jvKLqJb2X61pEUl/GwyIz/XOyR9LiJ2RsSuiLhE0qOSrii5rq5QXwXUJ+l7ZdeS1H2Sftz23jMbbL/RNp/PFtkelLRP0s0ZJ23MyM81JunPl2z7Yn37/e0vpyuc6ZFLkiX9TkQsllhPWhERtq+V9Enb75d0WtJjkt5XZl2Jnfls9kt6TtLnJH2i1Io2iOWHAJAcrRUASI4gB4DkCHIASI4gB4DkCHIASI4gB4DkCHIASO7/ATeuOjaldCnUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 箱线图\n",
    "def box_plots():\n",
    "    # 数据准备\n",
    "    # 生成0-1之间的20*4维度数据\n",
    "    data=np.random.normal(size=(10,4)) #正态分布numpy.random.normal(loc=0.0, scale=1.0, size=None)  loc均值  scale方差\n",
    "    print(data)#10行  每一行里面又4个元素\n",
    "    lables = ['A','B','C','D']\n",
    "    # 用Matplotlib画箱线图\n",
    "    plt.boxplot(data,labels=lables)\n",
    "    plt.title(\"use matplotlib\")\n",
    "    plt.show()\n",
    "    # 用Seaborn画箱线图\n",
    "    df = pd.DataFrame(data, columns=lables)\n",
    "    sns.boxplot(data=df)\n",
    "    plt.title(\"use seaborns\")\n",
    "    plt.show()\n",
    "box_plots()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 饼图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 饼图\n",
    "def pie_chart():\n",
    "    # 数据准备\n",
    "    nums = [25, 33, 37]\n",
    "    # 射手adc：法师apc：坦克tk\n",
    "    labels = ['ADC','APC', 'TK']\n",
    "    # 用Matplotlib画饼图\n",
    "    plt.pie(x = nums, labels=labels)\n",
    "    plt.title('use matplotlib')\n",
    "    plt.show()\n",
    "    #目前只找到了matplolib画饼图  seaborns没有发现\n",
    "pie_chart()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 饼图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 饼图\n",
    "def pie_chart2():\n",
    "    # 数据准备\n",
    "    data = {}\n",
    "    data['ADC'] = 25\n",
    "    data['APC'] = 33\n",
    "    data['TK'] = 37\n",
    "    data = pd.Series(data)\n",
    "    data.plot(kind = \"pie\", label='heros')\n",
    "    plt.show()\n",
    "pie_chart2()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 热力图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 热力图\n",
    "def thermodynamic():\n",
    "    # 数据准备\n",
    "    np.random.seed(33)\n",
    "    data = np.random.rand(3, 3)\n",
    "    heatmap = sns.heatmap(data)\n",
    "    plt.title('use seaborns')\n",
    "    plt.show()\n",
    "thermodynamic()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 后面还有几个图 暂未弄"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 子图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def f(t):\n",
    "    return np.exp(-t) * np.cos(2 * np.pi * t)\n",
    " \n",
    "t1 = np.arange(0, 5, 0.1)\n",
    "t2 = np.arange(0, 5, 0.02)\n",
    "\n",
    "plt.figure()\n",
    "plt.subplot(221)##2行2列 第1个\n",
    "plt.plot(t1, f(t1), 'r--')\n",
    "plt.subplot(222)##2行2列 第2个\n",
    "plt.plot(t2, np.cos(2 * np.pi * t2), 'r--')\n",
    "\n",
    "plt.subplot(212)##2行1列 第2个\n",
    "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
